Composite quantile regression for linear errors-in-variables models

Authors : Rong JİANG

Pages : 707-713

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Publication Date : 2015-06-01

Article Type : Research Paper

Abstract :Composite quantile regression can be more efficient and sometimes arbitrarily more efficient than least squares for non-normal random errors, and almost as efficient for normal random errors. Therefore, we extend composite quantile regression method to linear errors-in-variables models, and prove the asymptotic normality of the proposed estimators. Simulation results and a real dataset are also given to illustrate our the proposed methods.
Keywords : Errors in variables, composite quantile regression